1. Field of the Invention
The present invention relates to modeling signaling pathways in biological systems based on observations of consequences of perturbations, such as drug treatments or genetic alterations.
2. Description of the Related Art
In molecular biology, a targeted perturbation typically inhibits or activates the function of bio-molecules, e.g., as a result of drug action, small RNA interference, or genetic or epigenetic change. In a single experiment, targeted perturbations can be applied either singly or in combination. Combined perturbation by several agents can be much more informative than that by a single agent, as its effects typically reveal downstream genetic interactions (called epistasis) within the system, such as non-additive synergistic or antagonistic interactions. In addition, a large number of independently informative experiments can be performed if in each experiment a different small set of, e.g., two or three, perturbing agents (perturbants) is chosen from a larger arsenal. Thus, combinatorial perturbations are potentially powerful investigational tools for extracting information about pathways of molecular interactions in cells (such as A inactivates B, or X and Y are in the same pathway). (See, for example, Avery and Wasserman, “Ordering gene function: the interpretation of epistasis in regulatory hierarchies,” Trends Genet vol. 8, pp 312-316, 1992; Kelley and Ideker, “Systematic interpretation of genetic interactions using protein networks,” Nat Biotechnol, vol. 23, pp 561-566, 2005; Segre D, Deluna A, Church G M, Kishony R “Modular epistasis in yeast metabolism,” Nat Genet vol. 37, pp 77-83, 2005; Yeh P, Tschumi A I, Kishony R, “Functional classification of drugs by properties of their pairwise interactions,” Nat Genet vol. 38, pp 489-494, 2006; Lehár J, Zimmermann G R, Krueger A S, Molnar R A, Ledell J T, Heilbut A M, Short G F, Giusti L C, Nolan G P, Magid O A, Lee M S, Borisy A A, Stockwell B R, Keith C T, “Chemical combination effects predict connectivity in biological systems,” Mol Syst Biol vol. 3, p 80, 2007; Kaufman A, Keinan A, Meilijson I, Kupiec M, Ruppin E, “Quantitative analysis of genetic and neuronal multi-perturbation experiments,” PLoS Comput Biol vol. 1, e64, 2005).
Combinatorial perturbations can also be powerful application tools when rationally designed to achieve desired effects. For example, a combination of targeted drugs is considered a promising strategy to improve treatment efficacy, reduce off-target effects, and/or prevent evolutionary drug resistance. (See, for example, Borisy A A, Elliott P J, Hurst N. W, Lee M S, Lehar J, Price E R, Serbedzija G, Zimmermann G R, Foley M A, Stockwell B R, Keith C T, “Systematic discovery of multicomponent therapeutics,” Proc Natl Acad Sci USA vol. 100, pp 7977-7982, 2003; Chou T C, “Theoretical basis, experimental design, and computerized simulation of synergism and antagonism in drug combination studies,” Pharmacol Rev, vol. 58, pp 621-681, 2006; Keith C T, Borisy A A, Stockwell B R, “Multicomponent therapeutics for networked systems,” Nat Rev Drug Discov vol. 4, pp 71-78, 2005; Komarova N, Wodarz D, “Drug resistance in cancer: principles of emergence and prevention,” Proc Natl Acad Sci USA, vol. 102, pp 9714-9719, 2005).
With recent advances in molecular and computational technologies (e.g., targeted perturbation by small molecules, full-genome libraries of small RNAs, highly specific antibody assays, massive computer parallelization, and imaging techniques) there is intense interest in the investigational power of multiple perturbation experiments in a variety of biological systems. The inherent complexity of such experiments raises significant challenges in data analysis and an acute need for improving modeling approaches capable of capturing effects such as time-dependent responses, feedback effects and non-linear couplings.
Computer simulation of pathway models can be used to predict epistasis and other effects on cellular behavior only if such pathway models are well established means of predicting cellular behavior, i.e., if the pathways have been exhaustively tested with respect to their ability to predict experimental outcomes (see, for example, Lebar et al., 2007; Omholt SW, Plahte E, Oyehaug L, Xiang K, “Gene regulatory networks generating the phenomena of additivity, dominance and epistasis,” Genetics vol. 155, pp 969-980, 2000; Segre et al, 2005). In many situations, however, observational data is available but a valid pathway is unknown or is incompletely tested against experiment.